Development of a semi-empirical equilibrium model for downdraft gasification systems

Aydin, Ebubekir Sıddık; Yücel, Özgün; Sadıkoğlu, Hasan

doi:10.1016/j.energy.2017.04.132

Cited by 57 publications

(35 citation statements)

References 36 publications

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“…(15)] (Li et al, 2004), and one that estimates the amount of the total C converted to the gas phase (β), as given by [eq. (16)] (Aydin et al, 2017 ER (equivalence ratio) is the ratio of the air supplied to the system and the stoichiometric air requirement for complete combustion.…”

Section: Model Validationmentioning

confidence: 99%

“…Although the equilibrium is not reached in real cases, these models can predict the composition of syngas produced through gasification processes with satisfactory accuracy (Gambarotta et al, 2017). The models can be based on Gibbs Energy minimization (Fournel et al, 2015;Mendiburu et al, 2014;Jarungthammachote & Dutta, 2008) or use of equilibrium constants (Aydin et al, 2017;Mozafari et al, 2017;Jarungthammachote & Dutta, 2007). Altafini, Wander, and Barreto performed gasification tests using Pinus Elliotis sawdust as fuel (Altafini et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

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The Role of Char Formation in Equilibrium Modeling of Gasification Processes

2020

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This work aims to develop and validate a model based on chemical equilibrium to predict the composition of syngas produced by gasification of woody biomass (sawdust). The model uses Gibbs Energy minimization to evaluate the composition of the system in chemical equilibrium. The results are validated by comparison between experimental data from a gasification experiment and those of other similar models in the literature. The study shows good agreement among experimental results and elucidates the role of solid unburned carbon in such systems, which is present in considerable amount. It can be concluded that equilibrium models can be used to predict carbon conversion in gasification systems.

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Section: Model Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Role of Char Formation in Equilibrium Modeling of Gasification Processes

2020

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“…Different model studies have been employed to examine the effects of the variables affecting the gasification performance to evaluate product gas yield and composition . Depending on the complexity of the gasification, these models may be 3D fluid dynamical, artificial neural networks, artificial intelligence, kinetic‐based, or less complex equilibrium models . Even though none of the mathematical model is perfect for expressing or representing the gasification system, there is still a need for developing mathematical models .…”

Section: Introductionmentioning

confidence: 99%

“…11 Depending on the complexity of the gasification, these models may be 3D fluid dynamical, artificial neural networks, artificial intelligence, kinetic-based, or less complex equilibrium models. [12][13][14][15][16][17][18][19] Even though none of the mathematical model is perfect for expressing or representing the gasification system, there is still a need for developing mathematical models. 20 For computational fluid dynamics (CFD) models, a set of energy, momentum, and mass equations are solved to find the distribution of various parameters such as concentration and temperature over a certain region of a gasifier.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the different artificial neural networks in prediction of biomass gasification products

2019

Self Cite

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Summary In this study, artificial neural networks (ANNs) and a nonlinear autoregressive exogenous (NARX) neural network model were employed in order to model a fixed bed downdraft gasification. The relation between the feature group and the regression performance was investigated. First, feature group consists of the equivalence ratio (ER), air flow rate (AF), and temperature distribution (T0‐T5) obtained from the fixed bed downdraft gasifiers, while the second group includes ultimate and proximate values of biomasses, ER, AF, and the reduction temperature (T0). Models constructed to predict the syngas composition (H2, CO2, CO, CH4) and calorific value. Experimental gasification data that involve 3831 data samples that belong to pinecone and wood pellet were used for training the ANNs. Different ANN architecture and NARX time series model have been constructed to examine the prediction accuracy of the models. The results of the ANN models were consistent with the experimental data (R2 > 0.99). The overall score of NARX time series networks is found to be higher than other architecture types. A successful method is proposed to reduce the number of features, and the effect of the features on the prediction capability was examined by calculating the relative importance index using the Garson's equation.

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“…Equations 5 and 6 were employed in this work as auxiliary equations to solve the nonlinear equations system. These two reactions have been taken into account by Gagliano et al (2016) and Aydin et al (2017) to solve downdraft gasification models based on equilibrium assumptions. Additional gasification products, H2S and COS, are added to the model that are associated with the sulfur contained in the biomass.…”

Section: Model Descriptionmentioning

confidence: 99%

Effect of Torrefied Wood Biomass under an Oxidizing Environment in a Downdraft Gasification Process

Ramos-Carmona

Pérez

2017

BioResources

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The effect of composition and heating value of torrefied biomass under an oxidizing atmosphere at different conditions (180, 210, and 240 °C during 30, 75, and 120 minutes) was studied relative to downdraft gasification performance. An extended model for gasification in thermochemical equilibrium was used to evaluate the effect of pretreated biomasses, fuelto-air equivalence ratio, and char byproduct production on the producer gas composition, reaction temperature, cold gas efficiency (CGE), and the engine fuel quality (EFQ). The model was validated with experimental data, reaching a global relative error of 8.5%. For raw or torrefied biomasses, with regard to char production, the CGE decreases if char increases; this is due to the fact that the process tends to combustion regimes when a lower amount of carbon is involved in the gasification reaction. Otherwise, the CGE and EFQ increase (up to 80% and 2.5 MJ/kg, respectively) if fuel-to-air ratio increases. With regard to the torrefied biomass, it is highlighted that CGE and EFQ increase from 77% to 82% and from 2.2 MJ/kg to 2.5 MJ/kg, respectively, when the torrefaction conditions (temperature and/or time) increase. This behavior is related to the increase of the autothermal zones in the gasification process and due to the higher heating value of torrefied biomass.

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Development of a semi-empirical equilibrium model for downdraft gasification systems

Cited by 57 publications

References 36 publications

The Role of Char Formation in Equilibrium Modeling of Gasification Processes

The Role of Char Formation in Equilibrium Modeling of Gasification Processes

Comparison of the different artificial neural networks in prediction of biomass gasification products

Effect of Torrefied Wood Biomass under an Oxidizing Environment in a Downdraft Gasification Process

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